Loan Analysis by Ryan Neal
This project analyzes the prosper loan data set.
Univariate Plots Section
## ListingKey ListingNumber
## 17A93590655669644DB4C06: 6 Min. : 4
## 349D3587495831350F0F648: 4 1st Qu.: 400919
## 47C1359638497431975670B: 4 Median : 600554
## 8474358854651984137201C: 4 Mean : 627886
## DE8535960513435199406CE: 4 3rd Qu.: 892634
## 04C13599434217079754AEE: 3 Max. :1255725
## (Other) :113912
## ListingCreationDate CreditGrade Term
## 2013-10-02 17:20:16.550000000: 6 :84984 Min. :12.00
## 2013-08-28 20:31:41.107000000: 4 C : 5649 1st Qu.:36.00
## 2013-09-08 09:27:44.853000000: 4 D : 5153 Median :36.00
## 2013-12-06 05:43:13.830000000: 4 B : 4389 Mean :40.83
## 2013-12-06 11:44:58.283000000: 4 AA : 3509 3rd Qu.:36.00
## 2013-08-21 07:25:22.360000000: 3 HR : 3508 Max. :60.00
## (Other) :113912 (Other): 6745
## LoanStatus ClosedDate
## Current :56576 :58848
## Completed :38074 2014-03-04 00:00:00: 105
## Chargedoff :11992 2014-02-19 00:00:00: 100
## Defaulted : 5018 2014-02-11 00:00:00: 92
## Past Due (1-15 days) : 806 2012-10-30 00:00:00: 81
## Past Due (31-60 days): 363 2013-02-26 00:00:00: 78
## (Other) : 1108 (Other) :54633
## BorrowerAPR BorrowerRate LenderYield
## Min. :0.00653 Min. :0.0000 Min. :-0.0100
## 1st Qu.:0.15629 1st Qu.:0.1340 1st Qu.: 0.1242
## Median :0.20976 Median :0.1840 Median : 0.1730
## Mean :0.21883 Mean :0.1928 Mean : 0.1827
## 3rd Qu.:0.28381 3rd Qu.:0.2500 3rd Qu.: 0.2400
## Max. :0.51229 Max. :0.4975 Max. : 0.4925
## NA's :25
## EstimatedEffectiveYield EstimatedLoss EstimatedReturn
## Min. :-0.183 Min. :0.005 Min. :-0.183
## 1st Qu.: 0.116 1st Qu.:0.042 1st Qu.: 0.074
## Median : 0.162 Median :0.072 Median : 0.092
## Mean : 0.169 Mean :0.080 Mean : 0.096
## 3rd Qu.: 0.224 3rd Qu.:0.112 3rd Qu.: 0.117
## Max. : 0.320 Max. :0.366 Max. : 0.284
## NA's :29084 NA's :29084 NA's :29084
## ProsperRating..numeric. ProsperRating..Alpha. ProsperScore
## Min. :1.000 :29084 Min. : 1.00
## 1st Qu.:3.000 C :18345 1st Qu.: 4.00
## Median :4.000 B :15581 Median : 6.00
## Mean :4.072 A :14551 Mean : 5.95
## 3rd Qu.:5.000 D :14274 3rd Qu.: 8.00
## Max. :7.000 E : 9795 Max. :11.00
## NA's :29084 (Other):12307 NA's :29084
## ListingCategory..numeric. BorrowerState
## Min. : 0.000 CA :14717
## 1st Qu.: 1.000 TX : 6842
## Median : 1.000 NY : 6729
## Mean : 2.774 FL : 6720
## 3rd Qu.: 3.000 IL : 5921
## Max. :20.000 : 5515
## (Other):67493
## Occupation EmploymentStatus
## Other :28617 Employed :67322
## Professional :13628 Full-time :26355
## Computer Programmer : 4478 Self-employed: 6134
## Executive : 4311 Not available: 5347
## Teacher : 3759 Other : 3806
## Administrative Assistant: 3688 : 2255
## (Other) :55456 (Other) : 2718
## EmploymentStatusDuration IsBorrowerHomeowner CurrentlyInGroup
## Min. : 0.00 False:56459 False:101218
## 1st Qu.: 26.00 True :57478 True : 12719
## Median : 67.00
## Mean : 96.07
## 3rd Qu.:137.00
## Max. :755.00
## NA's :7625
## GroupKey DateCreditPulled
## :100596 2013-12-23 09:38:12: 6
## 783C3371218786870A73D20: 1140 2013-11-21 09:09:41: 4
## 3D4D3366260257624AB272D: 916 2013-12-06 05:43:16: 4
## 6A3B336601725506917317E: 698 2014-01-14 20:17:49: 4
## FEF83377364176536637E50: 611 2014-02-09 12:14:41: 4
## C9643379247860156A00EC0: 342 2013-09-27 22:04:54: 3
## (Other) : 9634 (Other) :113912
## CreditScoreRangeLower CreditScoreRangeUpper
## Min. : 0.0 Min. : 19.0
## 1st Qu.:660.0 1st Qu.:679.0
## Median :680.0 Median :699.0
## Mean :685.6 Mean :704.6
## 3rd Qu.:720.0 3rd Qu.:739.0
## Max. :880.0 Max. :899.0
## NA's :591 NA's :591
## FirstRecordedCreditLine CurrentCreditLines OpenCreditLines
## : 697 Min. : 0.00 Min. : 0.00
## 1993-12-01 00:00:00: 185 1st Qu.: 7.00 1st Qu.: 6.00
## 1994-11-01 00:00:00: 178 Median :10.00 Median : 9.00
## 1995-11-01 00:00:00: 168 Mean :10.32 Mean : 9.26
## 1990-04-01 00:00:00: 161 3rd Qu.:13.00 3rd Qu.:12.00
## 1995-03-01 00:00:00: 159 Max. :59.00 Max. :54.00
## (Other) :112389 NA's :7604 NA's :7604
## TotalCreditLinespast7years OpenRevolvingAccounts
## Min. : 2.00 Min. : 0.00
## 1st Qu.: 17.00 1st Qu.: 4.00
## Median : 25.00 Median : 6.00
## Mean : 26.75 Mean : 6.97
## 3rd Qu.: 35.00 3rd Qu.: 9.00
## Max. :136.00 Max. :51.00
## NA's :697
## OpenRevolvingMonthlyPayment InquiriesLast6Months TotalInquiries
## Min. : 0.0 Min. : 0.000 Min. : 0.000
## 1st Qu.: 114.0 1st Qu.: 0.000 1st Qu.: 2.000
## Median : 271.0 Median : 1.000 Median : 4.000
## Mean : 398.3 Mean : 1.435 Mean : 5.584
## 3rd Qu.: 525.0 3rd Qu.: 2.000 3rd Qu.: 7.000
## Max. :14985.0 Max. :105.000 Max. :379.000
## NA's :697 NA's :1159
## CurrentDelinquencies AmountDelinquent DelinquenciesLast7Years
## Min. : 0.0000 Min. : 0.0 Min. : 0.000
## 1st Qu.: 0.0000 1st Qu.: 0.0 1st Qu.: 0.000
## Median : 0.0000 Median : 0.0 Median : 0.000
## Mean : 0.5921 Mean : 984.5 Mean : 4.155
## 3rd Qu.: 0.0000 3rd Qu.: 0.0 3rd Qu.: 3.000
## Max. :83.0000 Max. :463881.0 Max. :99.000
## NA's :697 NA's :7622 NA's :990
## PublicRecordsLast10Years PublicRecordsLast12Months RevolvingCreditBalance
## Min. : 0.0000 Min. : 0.000 Min. : 0
## 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 3121
## Median : 0.0000 Median : 0.000 Median : 8549
## Mean : 0.3126 Mean : 0.015 Mean : 17599
## 3rd Qu.: 0.0000 3rd Qu.: 0.000 3rd Qu.: 19521
## Max. :38.0000 Max. :20.000 Max. :1435667
## NA's :697 NA's :7604 NA's :7604
## BankcardUtilization AvailableBankcardCredit TotalTrades
## Min. :0.000 Min. : 0 Min. : 0.00
## 1st Qu.:0.310 1st Qu.: 880 1st Qu.: 15.00
## Median :0.600 Median : 4100 Median : 22.00
## Mean :0.561 Mean : 11210 Mean : 23.23
## 3rd Qu.:0.840 3rd Qu.: 13180 3rd Qu.: 30.00
## Max. :5.950 Max. :646285 Max. :126.00
## NA's :7604 NA's :7544 NA's :7544
## TradesNeverDelinquent..percentage. TradesOpenedLast6Months
## Min. :0.000 Min. : 0.000
## 1st Qu.:0.820 1st Qu.: 0.000
## Median :0.940 Median : 0.000
## Mean :0.886 Mean : 0.802
## 3rd Qu.:1.000 3rd Qu.: 1.000
## Max. :1.000 Max. :20.000
## NA's :7544 NA's :7544
## DebtToIncomeRatio IncomeRange IncomeVerifiable
## Min. : 0.000 $25,000-49,999:32192 False: 8669
## 1st Qu.: 0.140 $50,000-74,999:31050 True :105268
## Median : 0.220 $100,000+ :17337
## Mean : 0.276 $75,000-99,999:16916
## 3rd Qu.: 0.320 Not displayed : 7741
## Max. :10.010 $1-24,999 : 7274
## NA's :8554 (Other) : 1427
## StatedMonthlyIncome LoanKey TotalProsperLoans
## Min. : 0 CB1B37030986463208432A1: 6 Min. :0.00
## 1st Qu.: 3200 2DEE3698211017519D7333F: 4 1st Qu.:1.00
## Median : 4667 9F4B37043517554537C364C: 4 Median :1.00
## Mean : 5608 D895370150591392337ED6D: 4 Mean :1.42
## 3rd Qu.: 6825 E6FB37073953690388BC56D: 4 3rd Qu.:2.00
## Max. :1750003 0D8F37036734373301ED419: 3 Max. :8.00
## (Other) :113912 NA's :91852
## TotalProsperPaymentsBilled OnTimeProsperPayments
## Min. : 0.00 Min. : 0.00
## 1st Qu.: 9.00 1st Qu.: 9.00
## Median : 16.00 Median : 15.00
## Mean : 22.93 Mean : 22.27
## 3rd Qu.: 33.00 3rd Qu.: 32.00
## Max. :141.00 Max. :141.00
## NA's :91852 NA's :91852
## ProsperPaymentsLessThanOneMonthLate ProsperPaymentsOneMonthPlusLate
## Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.00 1st Qu.: 0.00
## Median : 0.00 Median : 0.00
## Mean : 0.61 Mean : 0.05
## 3rd Qu.: 0.00 3rd Qu.: 0.00
## Max. :42.00 Max. :21.00
## NA's :91852 NA's :91852
## ProsperPrincipalBorrowed ProsperPrincipalOutstanding
## Min. : 0 Min. : 0
## 1st Qu.: 3500 1st Qu.: 0
## Median : 6000 Median : 1627
## Mean : 8472 Mean : 2930
## 3rd Qu.:11000 3rd Qu.: 4127
## Max. :72499 Max. :23451
## NA's :91852 NA's :91852
## ScorexChangeAtTimeOfListing LoanCurrentDaysDelinquent
## Min. :-209.00 Min. : 0.0
## 1st Qu.: -35.00 1st Qu.: 0.0
## Median : -3.00 Median : 0.0
## Mean : -3.22 Mean : 152.8
## 3rd Qu.: 25.00 3rd Qu.: 0.0
## Max. : 286.00 Max. :2704.0
## NA's :95009
## LoanFirstDefaultedCycleNumber LoanMonthsSinceOrigination LoanNumber
## Min. : 0.00 Min. : 0.0 Min. : 1
## 1st Qu.: 9.00 1st Qu.: 6.0 1st Qu.: 37332
## Median :14.00 Median : 21.0 Median : 68599
## Mean :16.27 Mean : 31.9 Mean : 69444
## 3rd Qu.:22.00 3rd Qu.: 65.0 3rd Qu.:101901
## Max. :44.00 Max. :100.0 Max. :136486
## NA's :96985
## LoanOriginalAmount LoanOriginationDate LoanOriginationQuarter
## Min. : 1000 2014-01-22 00:00:00: 491 Q4 2013:14450
## 1st Qu.: 4000 2013-11-13 00:00:00: 490 Q1 2014:12172
## Median : 6500 2014-02-19 00:00:00: 439 Q3 2013: 9180
## Mean : 8337 2013-10-16 00:00:00: 434 Q2 2013: 7099
## 3rd Qu.:12000 2014-01-28 00:00:00: 339 Q3 2012: 5632
## Max. :35000 2013-09-24 00:00:00: 316 Q2 2012: 5061
## (Other) :111428 (Other):60343
## MemberKey MonthlyLoanPayment LP_CustomerPayments
## 63CA34120866140639431C9: 9 Min. : 0.0 Min. : -2.35
## 16083364744933457E57FB9: 8 1st Qu.: 131.6 1st Qu.: 1005.76
## 3A2F3380477699707C81385: 8 Median : 217.7 Median : 2583.83
## 4D9C3403302047712AD0CDD: 8 Mean : 272.5 Mean : 4183.08
## 739C338135235294782AE75: 8 3rd Qu.: 371.6 3rd Qu.: 5548.40
## 7E1733653050264822FAA3D: 8 Max. :2251.5 Max. :40702.39
## (Other) :113888
## LP_CustomerPrincipalPayments LP_InterestandFees LP_ServiceFees
## Min. : 0.0 Min. : -2.35 Min. :-664.87
## 1st Qu.: 500.9 1st Qu.: 274.87 1st Qu.: -73.18
## Median : 1587.5 Median : 700.84 Median : -34.44
## Mean : 3105.5 Mean : 1077.54 Mean : -54.73
## 3rd Qu.: 4000.0 3rd Qu.: 1458.54 3rd Qu.: -13.92
## Max. :35000.0 Max. :15617.03 Max. : 32.06
##
## LP_CollectionFees LP_GrossPrincipalLoss LP_NetPrincipalLoss
## Min. :-9274.75 Min. : -94.2 Min. : -954.5
## 1st Qu.: 0.00 1st Qu.: 0.0 1st Qu.: 0.0
## Median : 0.00 Median : 0.0 Median : 0.0
## Mean : -14.24 Mean : 700.4 Mean : 681.4
## 3rd Qu.: 0.00 3rd Qu.: 0.0 3rd Qu.: 0.0
## Max. : 0.00 Max. :25000.0 Max. :25000.0
##
## LP_NonPrincipalRecoverypayments PercentFunded Recommendations
## Min. : 0.00 Min. :0.7000 Min. : 0.00000
## 1st Qu.: 0.00 1st Qu.:1.0000 1st Qu.: 0.00000
## Median : 0.00 Median :1.0000 Median : 0.00000
## Mean : 25.14 Mean :0.9986 Mean : 0.04803
## 3rd Qu.: 0.00 3rd Qu.:1.0000 3rd Qu.: 0.00000
## Max. :21117.90 Max. :1.0125 Max. :39.00000
##
## InvestmentFromFriendsCount InvestmentFromFriendsAmount Investors
## Min. : 0.00000 Min. : 0.00 Min. : 1.00
## 1st Qu.: 0.00000 1st Qu.: 0.00 1st Qu.: 2.00
## Median : 0.00000 Median : 0.00 Median : 44.00
## Mean : 0.02346 Mean : 16.55 Mean : 80.48
## 3rd Qu.: 0.00000 3rd Qu.: 0.00 3rd Qu.: 115.00
## Max. :33.00000 Max. :25000.00 Max. :1189.00
## ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 7622 rows containing non-finite values (stat_bin).
The vast majority of lenders seem to have no delinquencies. Let’s look at a log transformation to be sure.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 7622 rows containing non-finite values (stat_bin).
Indeed it is the case that the vast majority of people of 0 loans deliquent. We will compare deliquency to credit score in the bivariate section to see how deliquency affects credit.
Let us now see if there is a different pattern between delinquencies and delinquencies in the last 7 years.
## Warning: Removed 990 rows containing non-finite values (stat_bin).
Pretty skewed. Let us log tranform and try again.
## Warning: Removed 990 rows containing non-finite values (stat_bin).
##
## Pearson's product-moment correlation
##
## data: prosperLoanData$DelinquenciesLast7Years and prosperLoanData$AmountDelinquent
## t = 78.217, df = 106310, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2275783 0.2389463
## sample estimates:
## cor
## 0.2332703It was hard to tell without a log transformation. I am curious how amount delinquent and delinquencies are correlated. Which will be a better predictor of APR?
I am also curious about total credit. Let us look at that distribution now.
## Warning: Removed 7544 rows containing non-finite values (stat_bin).
Once again, we will have to log transform. It appears in many instances leaning data has a strong right skew.
## Warning: Removed 7544 rows containing non-finite values (stat_bin).
Now that is interesting! Bank credit appears to be a bi-modal distribution.A very large portion of the population has 0 available bank credit.Then there is a slow scale up before a sharp decline in credit.
Let us look at income range next
## Warning: Ignoring unknown parameters: binwidth, bins, pad
Very cool. Income is normally distributed.
## Warning: Removed 50174 rows containing non-finite values (stat_bin).
There are some pretty interesting spikes in this data. My guess is most lenders report hard numbers (e.g. $5,000), which causes spikes. With that said, it is hard to analyze the distribution of this data. Is stated monthly income normally distributed. Annual income seemed to be. Let us log transform the data and recheck.
## Warning: Removed 852 rows containing non-finite values (stat_bin).
Now this is pretty interesting. It looks like another bi-modal distribution with a spike at 0 and then a normal distribution tacked on.
Let us now look at credit score
Once again, we have a cohort at 0 and then a normal distribution on the right. In this instance, that makes sense. I did not know credit could be 0. For the most part, it looks like credit scores are between 450 - 850
I am also curious about the interest rate lenders pay. Let us check that out next
## Warning: Removed 25 rows containing non-finite values (stat_bin).
This is interesting. We still have a bi-modal distribution. Only now, the skewed peak is on the other side. It is likely there is an inverse correlation between BorrowerAPR and many of the features we checked earlier. We will confirm this later on.
Now, let us check out the distribution for original loan amount
It looks like it is very common to take out loans in 5,000 increments. 4,000 is the most common and $15,000 is the next most common. Now, I want to see how loan count changes by year. I created a variable called LoanYear for this.
## Min. 1st Qu. Median Mean 3rd Qu.
## "2005-06-13" "2008-06-13" "2012-06-13" "2011-06-28" "2013-06-13"
## Max.
## "2014-06-13"
This is not surprising. Loans dropped precipitously in 2009 - following the financial crisis. Loans reached their pre-crisis level in 2011 and increased until 2014. Below is how the chart above looks when segmented daily.
Univariate Analysis
What is the structure of your dataset?
Many of the plots in this data set have a bi-modal distribution with many people having very bad credit and then a normal distribution of credit. One really disappointing characteristic of this data set is many features have so many null values that they can not be used.
What is/are the main feature(s) of interest in your dataset?
I am most interested in predicting the interest rate, so BorrowerAPR.
What other features in the dataset do you think will help support your
I will examine how credit score, income, past delinquencies, and loan date affect BorrowerAPR
Did you create any new variables from existing variables in the dataset?
I created new variables for loan year and loan day. I also created a variable, SortedIncome, that creates a factor from IncomeRange.
Of the features you investigated, were there any unusual distributions?
Oh yes. As touched upon earlier, I expected a normal distribution on most variables. Instead, it appears as if there are a large number of negative (and positive outliers). I will explore this more with bivariate analysis.
Bivariate Plots Section
## Warning: Removed 591 rows containing missing values (geom_point).
##
## Pearson's product-moment correlation
##
## data: prosperLoanData$CreditScoreRangeUpper and prosperLoanData$BorrowerAPR
## t = -160.21, df = 113340, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4344422 -0.4249487
## sample estimates:
## cor
## -0.4297073Wow, this relationship is not as strong as I would have expected. Let us try income and APR
## Warning: Removed 25 rows containing missing values (geom_point).
##
## Pearson's product-moment correlation
##
## data: prosperLoanData$StatedMonthlyIncome and prosperLoanData$BorrowerAPR
## t = -27.884, df = 113910, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.08810353 -0.07656794
## sample estimates:
## cor
## -0.08233849The relationship is not as strong as I would have expected.
## Warning: Removed 25 rows containing non-finite values (stat_boxplot).
This looks a lot better. The weird thing here is $0 income has the lowest median APR, but as we saw earlier the sample size is small. The not weird thing is Not employed has the highest APR.
## Warning: package 'bindrcpp' was built under R version 3.4.4
Now, we are talking! To better understand the data I grouped everything by year. Here we can see a strong rise in APR by year following the 2008 crash and then a steep decline starting around 2011.
We also see a steep decline in loans during 2009 and a gradual increase through 2013. Let us try a similar grouping by income range and see if we get a similar result.
Hey look at that! A nice negative correlation. Now I would like to examine how credit usage affects interest rate
## Warning: Removed 25 rows containing missing values (geom_point).
Not a strong relationship. Let us try account balance instead.
It does not appear like there is a much of a relationship between credit balance and APR. Let us check out how loan amount affects APR.
## Warning: Removed 1 rows containing missing values (geom_point).
Again not a strong relationship. Let us see how loan amount affects APR.
## Warning: Removed 25 rows containing missing values (geom_point).
##
## Pearson's product-moment correlation
##
## data: prosperLoanData$LoanOriginalAmount and prosperLoanData$BorrowerAPR
## t = -115.14, df = 113910, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3280787 -0.3176752
## sample estimates:
## cor
## -0.3228867It looks like there is a slight negative relationship, but it is not that strong. Let us see how term affects APR.
## Warning: Removed 25 rows containing non-finite values (stat_boxplot).
Not a real strong relationship. 36 months is the most common term and has a lot of outliers. Let us check out if verifying income affects interest rate.
## Warning: Removed 25 rows containing non-finite values (stat_boxplot).
It does help to verify income. Finally let us see how delinquency affects APR
## Warning: Removed 990 rows containing missing values (geom_point).
This is not real strong. Out of curiosity I wonder how count of delinquencies correlates with amount.
ggplot(aes(x=DelinquenciesLast7Years, y=AmountDelinquent), data=prosperLoanData) +
geom_point() +
ylim(0, 10000)## Warning: Removed 10206 rows containing missing values (geom_point).
cor.test(prosperLoanData$DelinquenciesLast7Years, prosperLoanData$AmountDelinquent)##
## Pearson's product-moment correlation
##
## data: prosperLoanData$DelinquenciesLast7Years and prosperLoanData$AmountDelinquent
## t = 78.217, df = 106310, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2275783 0.2389463
## sample estimates:
## cor
## 0.2332703This is interesting there is a relationship between total delinquences and amount delinquent, but it is not as strong as I would expect.
ggplot(aes(x=AmountDelinquent, y=BorrowerAPR), data=prosperLoanData) +
geom_point()## Warning: Removed 7622 rows containing missing values (geom_point).
Once again, I am not seeing a strong relationship.
Bivariate Analysis
Tip: As before, summarize what you found in your bivariate explorations here. Use the questions below to guide your discussion.
Talk about some of the relationships you observed in this part of the project
In this section, we noticed some interesting relationships between variables and APR. In particular, we noticed APR varies greatly based on macroeconomic trends (i.e. APR changes based on year). In addition, APR also varies based on individual differences. Higher income individuals generally have a lower APR. We also learned that verifying income can also lower APR.
However, we also learned some things we might imagine affect APR do not. For example, neither open revolving accounts or account balance seemed to make a difference. Term length also did not have much effect. Whether delinquencies or loan amount affected APR was not conclusive
Did you observe any interesting relationships between the other features
(not the main feature(s) of interest)?
What was the strongest relationship you found?
The relationship between median APR and income range was very strong.
Multivariate Plots Section
Tip: Now it’s time to put everything together. Based on what you found in the bivariate plots section, create a few multivariate plots to investigate more complex interactions between variables. Make sure that the plots that you create here are justified by the plots you explored in the previous section. If you plan on creating any mathematical models, this is the section where you will do that.
Multivariate Analysis
Talk about some of the relationships you observed in this part of the
investigation. Were there features that strengthened each other in terms of
looking at your feature(s) of interest?
Were there any interesting or surprising interactions between features?
OPTIONAL: Did you create any models with your dataset? Discuss the
strengths and limitations of your model.
Final Plots and Summary
Tip: You’ve done a lot of exploration and have built up an understanding of the structure of and relationships between the variables in your dataset. Here, you will select three plots from all of your previous exploration to present here as a summary of some of your most interesting findings. Make sure that you have refined your selected plots for good titling, axis labels (with units), and good aesthetic choices (e.g. color, transparency). After each plot, make sure you justify why you chose each plot by describing what it shows.
Plot One
Description One
Plot Two
Description Two
Plot Three
Description Three
Reflection
Tip: Here’s the final step! Reflect on the exploration you performed and the insights you found. What were some of the struggles that you went through? What went well? What was surprising? Make sure you include an insight into future work that could be done with the dataset.
Tip: Don’t forget to remove this, and the other Tip sections before saving your final work and knitting the final report!